Concurrency Method For Forecasting Impact Of Speed Tiers On Consumption

ABSTRACT

A forecast model processes performance data from a site, e.g., a cable modem termination system (CMTS), to obtain a set of concurrency equations for existing speed tiers that is based on an observed subscriber bandwidth for the site. A new set of concurrency equations is obtained for new speed tiers so that a new subscriber bandwidth can be predicted for the new speed tiers. Based on the new subscriber bandwidth, expected subscriber growth, and changes in data consumption, the site is reconfigured with additional ports based on the forecast. This process can be repeated for the other sites. Sites may be grouped together based on the observed subscriber bandwidth. A new subscriber bandwidth may be predicted for the group with the new speed tiers so that additional ports can be configured for each of the sites in the group.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to, and is a continuation of,U.S. patent application Ser. No. 13/892,847, filed May 13, 2013, whichis a continuation of U.S. patent application Ser. No. 13/221,105, filedAug. 30, 2011, (now issued as U.S. Pat. No. 8,451,746) and entitled“Concurrency Method for Forecasting Impact of Speed Tiers onConsumption,” which claims priority to, and is a continuation of, U.S.patent application Ser. No. 12/504,394, filed Jul. 16, 2009, andentitled, “Concurrency Method for Forecasting Impact of Speed Tiers onConsumption,” (now issued as U.S. Pat. No. 8,018,869), all of which arehereby incorporated by reference as to their entireties for allpurposes.

TECHNICAL FIELD

Aspects of the embodiments relate to estimating the impact of new data(speed) tiers on a service provider's equipment, e.g., cable modemtermination systems (CTMSs).

BACKGROUND

A cable modem termination system (CMTS) is equipment typically found ina cable company's headend (hubsite) and is used to provide high speeddata services, e.g., cable internet or Voice over IP, to cablesubscribers. A CMTS often functions as a router with Ethernet interfaces(connections) on one side and coax RF interfaces on the other side. TheRF/coax interfaces may carry RF signals to and from the subscriber'scable modem.

CMTSs typically carry only IP traffic. Traffic destined for the cablemodem from the Internet, often designated as downstream traffic, iscarried in IP packets encapsulated in Moving Picture Experts Group(MPEG) transport stream packets. The MPEG packets are carried on datastreams that are typically modulated onto a TV channel using QuadratureAmplitude Modulation (QAM). Upstream data (data from cable modems to theheadend or Internet) is carried in Ethernet frames modulated with QPSK,16-QAM, 32-QAM, 64-QAM, or S-CDMA. Transmission is often through thesub-band portion of the cable TV spectrum (also known as the “T”channels), which is a lower part of the frequency spectrum than thedownstream signal.

In order to provide high speed data services, a cable company typicallyconnects its headend to the Internet via very high capacity data linksto a network service provider. On the subscriber side of the headend,the CMTS enables the communication with subscribers' cable modems.Different CMTSs are capable of serving different cable modem populationsize, ranging from 4,000 cable modems to 150,000 or more, depending inpart on traffic. A given headend may have between half a dozen to adozen or more CMTSs to service the cable modem population served by thatheadend or hybrid fiber coax (HFC) hub. CMTSs may have both Ethernetinterfaces as well as RF interfaces. In this way, traffic that is comingfrom the Internet can be routed through the Ethernet interface, throughthe CMTS and then onto the RF interfaces that are connected to the cablecompany's HFC hub. The traffic typically winds its way through the HFCto end up at the cable modem in the subscriber's home. Traffic goingfrom a subscriber's home systems go through the cable modem and out tothe Internet in the opposite direction.

Cable subscribers are typically assigned to a specific CMTS, in whicheach subscriber is provided grades of data services. It is thereforeimportant that the cable provider engineer the CMTSs so that subscribersexperience the expected quality of service.

BRIEF SUMMARY

The following presents a simplified summary of the disclosure in orderto provide a basic understanding of some aspects. It is not intended toidentify key or critical elements of the embodiments or to delineate thescope of the embodiments. The following summary merely presents someconcepts of the disclosure in a simplified form as a prelude to the moredetailed description provided below.

A forecast model processes performance data from a site, e.g., a cablemodem termination system (CMTS), to obtain a set of concurrencyequations for existing speed tiers that is based on an observedsubscriber bandwidth for the site. A new set of concurrency equations isobtained for new speed tiers, and a forecasted subscriber bandwidth ispredicted for the new speed tiers. Based on the forecasted subscriberbandwidth, expected subscriber growth, and changes in data consumption,the site is reconfigured with additional ports in accordance with theforecast model. This process can then be repeated for the other sites.

With another aspect of the embodiments, sites may be grouped togetherbased on the observed subscriber bandwidth. A forecasted subscriberbandwidth can be predicted for the group with the new speed tiers sothat additional ports can be configured for each of the sites in thegroup.

With another aspect of the embodiments, updated performance datareflects changed data consumption characteristics for a site.Consequently, concurrency coefficients for the existing speed tiers areupdated, and the number of ports for the site is re-evaluated.

Other embodiments may be partially or wholly implemented on acomputer-readable medium, for example, by storing computer-executableinstructions or modules, or by utilizing computer-readable datastructures.

Of course, the methods and systems of the above-referenced embodimentsmay also include other additional elements, steps, computer-executableinstructions, or computer-readable data structures. In this regard,other embodiments are disclosed and claimed herein as well.

The details of these and other embodiments are set forth in theaccompanying drawings and the description below. Other features andadvantages of the embodiments will be apparent from the description anddrawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1 shows a cable system that provides data services in accordancewith aspects of the embodiments.

FIG. 2 shows an exemplary relationship between concurrency and availablebandwidth in accordance with aspects of the embodiments.

FIG. 3 shows an exemplary relationship between concurrency andsubscriber bandwidth for various speed tiers in accordance with aspectsof the embodiments.

FIG. 4 shows an example for obtaining concurrency as a function ofsubscriber bandwidth in accordance with aspects of the embodiments.

FIG. 5 shows an example of observed subscriber bandwidth and calculatedsubscriber bandwidth in accordance with aspects of the embodiments.

FIG. 6 shows an exemplary concurrency curve for an existing 16 Mbps tierin accordance with aspects of the embodiments.

FIG. 7 shows an exemplary concurrency curve for an existing 8 Mbps tierin accordance with aspects of the embodiments.

FIG. 8 shows an exemplary concurrency curve for an existing 6 Mbps tierin accordance with aspects of the embodiments.

FIG. 9 shows an example for obtaining concurrency curves for new speedtiers in accordance with aspects of the embodiments.

FIG. 10 shows a flow diagram for determining a port configuration for acable modem termination system (CMTS) in accordance with aspects of theembodiments.

FIG. 11 shows a flow diagram for predicting the subscriber bandwidthwith new speed tiers from observed subscriber bandwidth with currentspeed tiers in accordance with aspects of the embodiments.

FIG. 12 shows an apparatus that supports planning CMTS configurationswith new speed tiers in accordance with aspects of the embodiments.

DETAILED DESCRIPTION

According to an aspect of the embodiments, the impact of changes tospeed (data) tier penetrations and service offers to bandwidthconsumption is forecasted. Traditional systems typically use either anassumed growth rate or a calculated growth rate from historical data.Consequently, traditional systems typically do not account for theintroduction of new speed tiers and the impact of the new speed tiers oncongested data ports of a service provider's equipment.

FIG. 1 shows cable system 100 that provides data services to a pluralityof subscribers in accordance with aspects of the embodiments. The cableprovider offers data services through hubsite (headend) 151 to eachsubscriber through an assigned cable modem (e.g., cable modems 103 and111) to the subscriber's equipment (e.g., equipment 101 and 103). Eachcable modem is connected to an assigned port (e.g., port 109 and 115) ofa cable modem termination system (e.g., CMTS 105), where ports may bestatically or dynamically assigned to subscribers to support dataservices. For example, port 109 may have a total data capacity of 3880Kbps. If the bandwidth per subscriber (subscriber bandwidth) is 50 Kbps,then the port can support approximately 77 subscribers (3880/50). EachCMTS is connected to the Internet through an Internet interface (e.g.,Internet interface 107) to provide connectivity to the Internet. TheInternet typically provides connectivity to websites and well asprovides connectivity to other CMTSs.

While FIG. 1 only illustrates one cable modem termination system (CMTS105), cable system 100 typically includes a plurality of CMTSs (datasites) that may number in the hundreds or thousands of data sites andthat may be geographically dispersed. Also, CMTS 105 typically supportsa plurality to ports, e.g., ports 109 and 115. (While FIG. 1 does notexplicitly show subscribers being assigned to port 115, port 115 istypically assigned the same number of subscribers as port 109.) Forexample, if CMTS 105 is engineered to support 5000 subscribers withports that can each support 3880 Kbps (as described above), then CMTS105 would need to be equipped with at least 65 ports.

CMTS 105 is typically scalable, i.e., adding a port increases thesubscriber capacity for a given subscriber bandwidth by a fixedincremental amount (e.g., 77 subscribers or a total bandwidth capabilityof 3880 Kbps as discussed above). However, when the scalable limits arereached, another CMTS may be added to hubsite 151.

FIG. 2 shows exemplary relationship 200 between concurrency andavailable bandwidth in accordance with aspects of the embodiments. Theconcurrency is often defined as fraction or percentage of assignedsubscribers that are active at a given time (i.e., simultaneouslyactive). In general for a fixed consumption, a data speed increaseresults in shorter bursts and consequently lowers the concurrency.

Each subscriber is typically assigned to a speed (data) tier, in whichthe subscriber is limited to an average maximum data rate. For example,if a subscriber is assigned to an 8 Mbps speed tier, the subscriber islimited to an average maximum data rate of 8 Mbps, although thesubscriber may utilize more than 8 Mbps at a particular time instance.During a peak time, each subscriber on average may consume a measuredbandwidth (referred to as bandwidth per subscriber or the subscriberbandwidth).

With exemplary relationship 200, the concurrency for either the uplinkor downlink increases as the available bandwidth of the assigned portdecreases. In other words, as more bandwidth is available, a subscriberis able to download or upload files faster so that the fraction ofsubscribers is smaller.

FIG. 3 shows exemplary relationship 300 between concurrency andsubscriber bandwidth for various speed tiers in accordance with aspectsof the embodiments. As will be discussed, existing speed tiers mayinclude 6 Mbps (corresponding to curve 305), 8 Mbps (curve 304), and 16Mbps (curve 303). The cable provider may migrate subscribers to higherspeed tiers in order to be more competitive with competing dataservices. Consequently, as discussed in an exemplary embodiment, newspeed tiers may be offered at 22 Mbps (curve 302) and 50 Mbps (curve301). As will be discussed, concurrency curves for the new speed tiers(curves 301 and 302) may be predicted based on the concurrency curvesfor the existing speed tiers. In general, as illustrated in FIG. 3, theconcurrency decreases with higher speed tiers and increases as thebandwidth per subscriber increases.

FIG. 3 suggests that as the congestion increases, the concurrency tendsto increases. This observation is intuitively appealing because dataretransmission typically increases with greater congestion.

FIG. 4 shows an example for obtaining concurrency coefficients as afunction of subscriber bandwidth in accordance with aspects of theembodiments. The concurrency may be expressed as:

$\begin{matrix}{{concurrent\_ bandwidt} = {\sum\limits_{i = 1}^{N}{c_{i}*p_{i}*s_{i}}}} & \left( {{EQ}.\mspace{14mu} 1} \right)\end{matrix}$

where N is the number of existing speed tiers, p is the penetration forthe i^(th) speed tier, and s is the speed for the i^(th) speed tier. Inthe example shown in FIG. 4, the existing speed tiers are 16 Mbps, 8Mbps, 6 Mbps, 4 Mbps, and 0768 Mbps. The impact of 4 Mbps and 0.768 Mbpsis deemed as being small so that the 4 Mbps and 0.768 Mbps are ignoredin the exemplary embodiment. However, embodiments may includeconcurrency coefficients for each of the 5 speed tiers. Consequently,only speed tiers (s_(i)) are included in EQ. 1. In order to determinethe concurrency coefficients for the existing speed tiers, the activesubscribers per speed tier (p_(i)) and utilized bandwidth per subscriber(concurrent_bandwidth) are observed at a peak timeframe for each site(CMTS). A set of N simultaneous equations having a form as shown in EQ.1 may be solved to obtain the N unknown concurrency coefficients. Withthe exemplary embodiment having existing speed tiers 16 Mbps, 8 Mbps,and 6 Mbps, there are three concurrency coefficients; thus at leastthree simultaneous equations are needed to solve for the concurrencycoefficients.

Referring to FIG. 4, sites are grouped into a plurality of groups basedon the observed bandwidth per subscriber. For example, concurrencycoefficients 403, 404, and 405 are determined for group 401 andconcurrency coefficients 406, 407, and 408 are determined for group 402.With the exemplary embodiment, groups are distinguished from each otherby sufficiently different observed subscriber bandwidths.

To illustrate calculations using EQ. 1, assume that the observedbandwidth per subscriber is 76 Kbps, where 80%, 15%, and 5% of thesubscribers are assigned to 6 Mbps, 8 Mbps, and 16 Mbps speed tiers,respectively. The corresponding concurrency equation is:

76 Kbps=c1*0.8*6 Mbps+c2*0.15*8 Mbps+c3*0.05*16 Mbps  (EQ. 2)

or

1=63.2*c1+15.8*c2+10.5*c3  (EQ. 3)

Sites with a similar observed bandwidth per subscriber are groupedtogether to obtain a sufficient number of simultaneous equations tosolve for the concurrency coefficients. In this example, threesimultaneous equations are necessary to solve for three unknowns. Asshown in FIG. 4, Microsoft Office Excel® may be used to solve for theconcurrency coefficients.

The applicability of EQ. 1 in FIG. 1 is illustrated in the followingexample to determine calculated bandwidth 409. Concurrency coefficients406, 407, and 408 are approximately 3.433283, 13.87736, and 14.95212,respectively. (As shown in FIG. 4, the concurrency coefficients aremultiplied by 100 for mathematical expediency. Consequently, calculatedbandwidths are divided by 1000.) Applying EQ. 1:

cal_(—) bw*1000≈3.43*0.033*16M+13.88*0.053*8M+14.95*0.74*6M  (EQ. 4)

cal_(—) bw≈74.07 Kbps  (EQ. 5)

The value of EQ. 5 is slightly different from calculated BW 409 becausethe effects of the 4 Mbps and 0.768 Mbps tiers are ignored in the abovecalculation.

FIG. 5 shows an example of observed subscriber bandwidth and calculatedsubscriber bandwidth in accordance with aspects of the embodiments.Sites are ranked ordered by increasing value of bandwidth persubscriber, where curve 403 corresponds to the observed bandwidth persubscriber and curve 401 corresponds to the calculated bandwidth persubscriber (based on the determined concurrency coefficients). Curves403 a and 401 a, 403 b and 401 b, and 403 c and 401 c correspond to thelow range, middle range, and high range of the sites, respectively.While function 401 is well behaved in the middle range, there is somedivergence in the low and high ranges. However, the difference betweenthe observed bandwidth and calculated bandwidth is typicallysufficiently small. With the example shown in FIG. 5, the predictedbandwidth is ±15% for 94% of the sites.

In addition to applying a growth rate to per subscriber usage at a site,some embodiments may use a site's particular characteristics to solvefor concurrencies per speed tier. From the per site concurrencies,system 1200 (as shown in FIG. 12) estimates the existing usage based onthe number of existing subscribers per speed tier and the usage patternsof the speed tiers. For example, system 1200 may collect updated data(e.g., observed subscriber bandwidth) from CMTS 105 and recalculate theconcurrency coefficients to re-characterize the usage characteristics ofsubscribers. For example, subscribers may be using new data servicesthat impact an engineered CMTS based on an older set of assumptions. Inaddition, the concurrencies may then be used to estimate the impact ofintroducing new speed tiers or penetration changes among the existingspeed tiers. Consequently, system 1200 may estimate both existing usageand future usage based on a particular mix of speed tiers andpenetrations unique to a particular site.

FIGS. 6, 7, and 8 show exemplary concurrency curves 600, 700, and 800for 16 Mbps, 8 Mbps, and 6 Mbps tiers, respectively, in accordance withaspects of the embodiments. With some embodiments, for each of theconcurrency curves, data points are obtained from the determinedconcurrency coefficients for the site groups as exemplified in FIG. 4.For example, point 701 corresponds to concurrency coefficient 407 (withan approximate value of 13.9) and point 801 corresponds to concurrencycoefficient 408 (with an approximate value of 15.0), where group 402 ischaracterized by a bandwidth per subscriber of approximately 85 Kbps.The other data points may be determined from the concurrencycoefficients for other groups that are characterized by different valuesof subscriber bandwidths.

With some embodiments, Microsoft Office Excel® is used to fitconcurrency curves through the obtained data points, where theconcurrency curves have the form of a*e^(bx), and where a and b areconstants, x is the value of subscriber bandwidth, and y is theconcurrency*1000.

With the exemplary embodiment shown in FIGS. 6, 7, and 8, fitted curves600, 700, and 800 have exponential forms. Regressions are all above 95%R-squared. As the observed subscriber bandwidth increases, theconcurrency per speed tier increases. The increase in concurrency isproportionally larger as the subscriber bandwidth increases. Thischaracteristic is intuitively appealing because higher speed tiers aremore influenced by congestion. In other words, it takes longer foreveryone to do everything as the communication pipe gets congested.

FIG. 9 shows an example for obtaining concurrency curves 963 and 964 fornew speed tiers in accordance with aspects of the embodiments. Speedtier curves 961 and 962 (which relate the concurrency for differentspeed tiers including existing speed tiers and new speed tiers) arederived from concurrency curves 600, 700, and 800 for a plurality ofsubscriber bandwidths (e.g., 129 Kbps and 44 Kbps). For example, points901 and 904 are obtained from concurrency curve 600 (16 Mbps tier).Points 902 and 905 are obtained from concurrency curve 700 (8 Mbpstier). Points 903 and 906 are obtained from concurrency curve 800 (6Mbps tier).

Speed tier curves 961 and 962 (as well as other speed tier curves forother subscriber bandwidths not explicitly shown in FIG. 9) are extendedto other speed tiers (including the new speed tiers) by fitting a curve(e.g., with the form a*e^(bx)), through the points obtained from curves600, 700, and 800 to obtain points 907 and 908 (extrapolated curve 961)and points 909 and 910 (extrapolated curve 962).

New concurrency curves 963 and 964 are constructed from the newestimates (907, 908, 909, and 910 as well as points corresponding toother subscriber bandwidths not explicitly shown in FIG. 9). Forexample, points 911, 912, 913, and 914 are obtained from points 908(mapping 951), 910 (mapping 953), 907 (mapping 952), and 909 (mapping954), respectively. As will be further discussed, concurrencycoefficients for the new speed tiers (22 Mbps tier and 50 Mbps tier) canthen be obtained from concurrency curves 963 and 964.

FIG. 10 shows flow diagram 1000 for determining a port configuration fora cable modem termination system (CMTS) in accordance with aspects ofthe embodiments. Process 1000 is directed to CMTS budget forecasting andestimating the impact of new speed tier launches on the CMTS plant.

Input 1001 provides site data (as exemplified in FIG. 4) so thatconcurrency equations 1003 for existing speed tiers and new speed tierscan be determined. As will be further discussed in an illustrativeexample, the forecasted subscriber bandwidth 1002 (i.e., with the newspeed tiers) is determined for each of the sites based on the observedsubscriber bandwidth. With some embodiments, sites may be groupedtogether based on bandwidth characteristics (e.g., groups 401 and 402 asshown in FIG. 4) in order to facilitate forecasting efforts.

Forecast model 1004 utilizes concurrency equations derived for the newspeed tiers as well as assumptions 1007 about subscriber growth,consumption patterns, tier penetration, and cost per port. Consequently,the number of devices (subscribers) per port is forecasted. Someembodiments may make further assumptions to facilitate the forecastmodel. For example, typical usage growth may be assumed with expectedconsumption growth based on new applications that do not radicallydepart from the resource demands of current applications. However withsome future applications (e.g., a bandwidth-intensive video service),consumption patterns may dramatically alter the required subscriberbandwidth. If that may be the case, closed loop forecasting any be usedto counter disruptions. Closed loop forecasting may use observedbehavior patterns and observed performance impacts of prior changes toforecast future changes. This approach is akin to a feedback loop in anamplifier, in which the feedback is intended to reduce the error infuture estimates.

If step 1005 determines that the current numbers of ports cannotaccommodate the expected subscriber group and forecasted subscriberbandwidth, additional ports are added to the CMTS in step 1006.Consequently, total cost 1008 for upgrading a site (CMTS) can beforecasted from the number of added ports and the cost per port.

The following example illustrates process 1000. Referring to FIG. 4(entry 410), assume that the current subscriber bandwidth is 86 Kbps.With a total port bandwidth of 3880 Kbps, each port can accommodate 45subscribers with the current speed tiers of 16 Mbps, 8 Mbps, and 6 Mbpsspeed tiers, where the impact of the 4 Mbps and 0.768 Mbps speed tiersare ignored. Assuming that the site currently supports 5000 subscribers,111 ports are needed.

With the site upgrade, the example assumes that all of the 16 Mbpssubscribers migrate to the 50 Mbps tier while all of the othersubscribers migrate to the 22 Mbps tier. In other words, 10% of thesubscribers are assigned to the 50 Mbps tier and 90% of the subscribersare assigned to the 22 Mbps tier. Referring to FIG. 9, the concurrencycoefficients for the new speed tiers with a current subscriber bandwidthof 86 Kbps are approximately 0.15 (corresponding to 50 Mbps tier) and3.0 (corresponding to 22 Mbps tier. Using EQ. 1, the new subscriberbandwidth is determined by:

1000*forecasted_sub_(—) BW=0.1*01.5*50 Mbps+0.9*3.0*22 Mbps  (EQ. 6)

forecasted_sub_(—) BW=60 Kbps  (EQ. 7)

The above example illustrates a reduction of subscriber bandwidth withhigher speed tiers because of an increased efficiency resulting from areduction of congestion. In other words, if subscribers do not changetheir behavior but can do what they were doing faster, then the effectshould be less congestion

The example further assumes a subscriber growth of 25% (6000subscribers) and a subscriber bandwidth increase of 25% (75 Kbps) toaccommodate new applications. Forecast model 1004 predicts that eachport can support 50 subscribers (3880/75) and consequently 120 ports(6000/50) are needed. In other words, 9 ports need to be added to thesite. The above example can then be extended to the other sites.

FIG. 11 shows flow diagram 1100 for predicting the subscriber bandwidthwith new speed tiers from observed subscriber bandwidth with currentspeed tiers in accordance with aspects of the embodiments. In step 1101,data is collected for the different sites (FIG. 4) so that concurrencyequations (EQ. 1) can be determined in step 1103. In step 1105,concurrency curves are constructed for the existing speed tiers (e.g., 6Mbps, 8 Mbps, and 16 Mbps) as shown in FIGS. 6, 7, and 8.

In step 1107, speed tier curves (curves 961 and 962 as shown in FIG. 9)are extrapolated for different subscriber bandwidths in order to obtainnew estimates for the new speed tiers. The new estimates are then mappedto new concurrency curves (curves 963 and 964) for the new speed tiersin step 1109. Concurrency coefficients are than obtained from thecurrent subscriber bandwidth so that the forecasted subscriber bandwidthcan be predicted using EQ. 1 in step 1111. Consequently, the number ofsubscribers per port can be forecasted for the new tiers. Assumingsubscriber growth and forecasted consumption pattern, the requirednumber of ports is determined for each site in step 1113.

FIG. 12 shows apparatus 1200 that supports planning CMTS configurationswith new speed tiers in accordance with aspects of the embodiments. Withsome embodiments, apparatus 1200 comprises a computer platform thatsupports processes 1000 (FIG. 10) and 1100 (FIG. 11) as disclosedherein.

Apparatus 1200 interfaces to a plurality of cable mobile terminationsystems (e.g., CMTS 105 as shown in FIG. 1) through cable interface 1203to obtain observed performance data (e.g., observed bandwidth persubscriber), which may be stored in memory 1207. Apparatus 1200 may usethe observed performance data so that processor 1201 can determinewhether additional data ports should be added the CMTS by executingprocesses 1000 and 1100 in order to handle forecasted data traffic whennew speed tiers are introduced.

Apparatus 1200 determines the number of ports that are required of aCMTS as discussed herein and configures the CMTS through 1203 inaccordance with the forecast.

With some embodiments, a user may interact with processes 1000 and 1100through user interface 1205. For example, a user may specify thegrouping of sites (e.g., CMTSs) according to observed values ofbandwidth per subscriber and obtain concurrency coefficients forexisting speed tiers by executing Microsoft Office Excel® as shown inFIG. 4. The user may further execute Microsoft Office Excel® to obtainnew concurrency coefficients to determine the subscriber bandwidth withthe new speed tiers.

Processor 1201 may execute computer executable instructions from acomputer-readable medium, e.g., memory 1209. Computer storage media mayinclude volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules orother data.

While the exemplary embodiments have been discussed in broad terms of acable communications networking environment, the embodiments may beconfigured for other networking environments includingtelecommunications environments.

1. (canceled)
 2. A method comprising: receiving, by a computing device,an observed bandwidth usage per subscriber at each site of a pluralityof sites; calculating, by the computing device, a forecasted bandwidthusage per subscriber at each of the plurality of sites; ranking theplurality of sites based at least in part on the observed bandwidthusage per subscriber at each site, resulting in ranked sites; andmodifying a port configuration of a computing device on a serviceprovider network at a ranked site based on at least one calculated usagecurve corresponding to the ranked sites.
 3. The method of claim 2,wherein the at least one calculated usage curve comprises a firstbandwidth usage curve corresponding to the observed bandwidth usage persubscriber at each site of the plurality of sites and a second bandwidthusage curve corresponding to the forecasted bandwidth usage persubscriber at each site of the plurality of sites.
 4. The method ofclaim 2, wherein the forecasted bandwidth usage per subscriber iscalculated based on a plurality of concurrency coefficients from theobserved bandwidth usage per subscriber at each site of the rankedsites, wherein the plurality of concurrency coefficients correspond to aplurality of speed tiers of the service provider network.
 5. The methodof claim 2, wherein ranking the plurality of sites comprises ranking theplurality of sites based at least in part on a value of bandwidth persubscriber per site.
 6. The method of claim 2, wherein the ranking theplurality of sites comprises ranking the plurality of sites based atleast in part on the at least one calculated usage curves.
 7. The methodof claim 2, wherein the computing device is located at a headend of theservice provider network.
 8. The method of claim 2, wherein thecomputing device comprises a modem termination system having a pluralityof ports coupled to a plurality of modems.
 9. The method of claim 2,further comprising determining an additional number of needed ports forthe computing device, wherein modifying the port configuration of thecomputing device at each ranked site comprises configuring the computingdevice to support the additional number of needed ports.
 10. The methodof claim 2, wherein modifying the port configuration comprisesdetermining a quantity of ports of the computing device.
 11. A methodcomprising: retrieving a set of site characteristics corresponding to afirst speed tier and a second speed tier, wherein the set of sitecharacteristics comprises speed tier penetrations and bandwidth persubscriber data for a particular site; determining concurrency data forthe first speed tier and the second speed tier based on the set of sitecharacteristics; determining projected concurrency data for a thirdspeed tier based on the concurrency data for the first speed tier andthe second speed tier; ranking the particular site in a plurality ofsites based on the projected concurrency data; and modifying a portconfiguration of a computing device on a service provider network basedon the projected concurrency data.
 12. The method of claim 11, furthercomprising: calculating a forecasted bandwidth per subscriber for thethird speed tier, using the projected concurrency data for the thirdspeed tier.
 13. The method of claim 12, further comprising: forecastinga number of subscribers per port for the third speed tier, based on theforecasted bandwidth per subscriber for the third speed tier.
 14. Themethod of claim 11, further comprising: determining an additional numberof needed ports for the computing device, wherein modifying the portconfiguration comprises configuring the computing device to support anadditional number of needed ports.
 15. The method of claim 11, whereinthe concurrency data for the first speed tier and the second speed tiercomprises at least two concurrency curves, and wherein determining theprojected concurrency data for the third speed tier comprisesextrapolating a third concurrency curve for the third speed tier basedon the at least two concurrency curves.
 16. The method of claim 11,wherein determining concurrency data for the first speed tier and thesecond speed tier comprises determining concurrency coefficients basedon a current subscriber bandwidth data and a current speed tierpenetration.
 17. The method of claim 16, further comprising:constructing concurrency curves for each of the first speed tier and thesecond speed tier, based on the concurrency coefficients.
 18. The methodof claim 11, wherein the computing device is located at a headend of theservice provider network.
 19. A method comprising: receiving, by acomputing device, an observed bandwidth usage per subscriber for eachservice population in a plurality of service populations, wherein eachservice population is serviced via a different network device in aplurality of network devices; calculating, by the computing device, atleast one usage curve for each service population; ranking the pluralityof service populations based at least in part on the observed bandwidthusage per subscriber for each service population, resulting in rankedservice populations; and modifying a port configuration of a networkdevice servicing a ranked service population based on at least one usagecalculated curve corresponding to the ranked service population.
 20. Themethod of claim 19, wherein the at least one calculated usage curvecomprises a first bandwidth usage curve corresponding to the observedbandwidth usage per subscriber for each service population and a secondbandwidth usage curve corresponding to a forecasted bandwidth usage persubscriber for each service population.
 21. The method of claim 20,wherein the forecasted bandwidth usage per subscriber is calculatedbased on a plurality of concurrency coefficients from the observedbandwidth usage per subscriber for each service population, wherein theplurality of concurrency coefficients correspond to a plurality of speedtiers of a service provider network.